diff --git a/README.md b/README.md
index 00d0bc2a..ebfe0957 100644
--- a/README.md
+++ b/README.md
@@ -84,6 +84,7 @@ To date, the following post-processing methods have been implemented:
 
 - Temperature, Vector, & Matrix scaling - [Tutorial](https://torch-uncertainty.github.io/auto_tutorials/tutorial_scaler.html)
 - Monte Carlo Batch Normalization - [Tutorial](https://torch-uncertainty.github.io/auto_tutorials/tutorial_mc_batch_norm.html)
+- A wrapper for Laplace appoximation using the [Laplace library](https://github.com/aleximmer/Laplace)
 
 ## Tutorials
 
diff --git a/docs/source/api.rst b/docs/source/api.rst
index d4f99acf..a3762b92 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -242,6 +242,16 @@ Post-Processing Methods
 
 .. currentmodule:: torch_uncertainty.post_processing
 
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+    :template: class_inherited.rst
+    MCBatchNorm
+    Laplace
+    
+Scaling Methods
+^^^^^^^^^^^^^^^
+
 .. autosummary::
     :toctree: generated/
     :nosignatures:
@@ -250,7 +260,6 @@ Post-Processing Methods
     TemperatureScaler
     VectorScaler
     MatrixScaler
-    MCBatchNorm
 
 Datamodules
 -----------
diff --git a/docs/source/references.rst b/docs/source/references.rst
index bd4467c9..b219eb3f 100644
--- a/docs/source/references.rst
+++ b/docs/source/references.rst
@@ -193,6 +193,16 @@ For Monte-Carlo Batch Normalization, consider citing:
 * Authors: *Mathias Teye, Hossein Azizpour, and Kevin Smith*
 * Paper: `ICML 2018 <https://arxiv.org/pdf/1802.06455.pdf>`__.
 
+Laplace Approximation
+^^^^^^^^^^^^^^^^^^^^^
+
+For Laplace Approximation, consider citing:
+
+**Laplace Redux - Effortless Bayesian Deep Learning**
+
+* Authors: *Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig*
+* Paper: `NeurIPS 2021 <https://arxiv.org/abs/2106.14806>`__.
+
 Metrics
 -------
 
diff --git a/pyproject.toml b/pyproject.toml
index 0b11a230..822924d0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,7 +46,7 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-image = ["scikit-image", "h5py",]
+image = ["scikit-image", "h5py", "webdataset"]
 tabular = ["pandas"]
 dev = [
     "torch_uncertainty[image]",
@@ -63,7 +63,10 @@ docs = [
     "sphinx-design",
     "sphinx-codeautolink",
 ]
-all = ["torch_uncertainty[dev,docs,image,tabular]"]
+all = [
+    "torch_uncertainty[dev,docs,image,tabular]",
+    "laplace-torch"
+    ]
 
 [project.urls]
 homepage = "https://torch-uncertainty.github.io/"
diff --git a/torch_uncertainty/post_processing/laplace.py b/torch_uncertainty/post_processing/laplace.py
new file mode 100644
index 00000000..e3f19d13
--- /dev/null
+++ b/torch_uncertainty/post_processing/laplace.py
@@ -0,0 +1,68 @@
+from importlib import util
+from typing import Literal
+
+from torch import Tensor, nn
+from torch.utils.data import Dataset
+
+if util.find_spec("laplace"):
+    from laplace import Laplace
+
+    laplace_installed = True
+
+
+class Laplace(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        task: Literal["classification", "regression"],
+        subset_of_weights="last_layer",
+        hessian_structure="kron",
+        pred_type: Literal["glm", "nn"] = "glm",
+        link_approx: Literal[
+            "mc", "probit", "bridge", "bridge_norm"
+        ] = "probit",
+    ) -> None:
+        """Laplace approximation for uncertainty estimation.
+
+        This class is a wrapper of Laplace classes from the laplace-torch library.
+
+        Args:
+            model (nn.Module): model to be converted.
+            task (Literal["classification", "regression"]): task type.
+            subset_of_weights (str): subset of weights to be considered. Defaults to
+                "last_layer".
+            hessian_structure (str): structure of the Hessian matrix. Defaults to
+                "kron".
+            pred_type (Literal["glm", "nn"], optional): type of posterior predictive,
+                See the Laplace library for more details. Defaults to "glm".
+            link_approx (Literal["mc", "probit", "bridge", "bridge_norm"], optional):
+                how to approximate the classification link function for the `'glm'`.
+                See the Laplace library for more details. Defaults to "probit".
+
+        Reference:
+            Daxberger et al. Laplace Redux - Effortless Bayesian Deep Learning. In NeurIPS 2021.
+        """
+        super().__init__()
+        if not laplace_installed:
+            raise ImportError(
+                "The laplace-torch library is not installed. Please install it via `pip install laplace-torch`."
+            )
+        self.la = Laplace(
+            model=model,
+            task=task,
+            subset_of_weights=subset_of_weights,
+            hessian_structure=hessian_structure,
+        )
+        self.pred_type = pred_type
+        self.link_approx = link_approx
+
+    def fit(self, dataset: Dataset) -> None:
+        self.la.fit(dataset=dataset)
+
+    def forward(
+        self,
+        x: Tensor,
+    ) -> Tensor:
+        return self.la(
+            x, pred_type=self.pred_type, link_approx=self.link_approx
+        )
diff --git a/torch_uncertainty/post_processing/mc_batch_norm.py b/torch_uncertainty/post_processing/mc_batch_norm.py
index d99fdd7c..66d21889 100644
--- a/torch_uncertainty/post_processing/mc_batch_norm.py
+++ b/torch_uncertainty/post_processing/mc_batch_norm.py
@@ -99,7 +99,7 @@ def _est_forward(self, x: Tensor) -> Tensor:
     def forward(
         self,
         x: Tensor,
-    ) -> tuple[Tensor, Tensor]:
+    ) -> Tensor:
         if self.training:
             return self.model(x)
         if not self.trained: