  Robust uncertainty estimations are necessary in safety-critical applications
of Deep Learning. One such example is the semantic segmentation of medical
images, whilst deep-learning approaches have high performance in such tasks
they lack interpretability as they give no indication of their confidence when
making classification decisions. Robust and interpretable segmentation is a
critical first stage in automatically screening for pathologies hence the
optimal solution is one which can provide high accuracy but also capture the
underlying uncertainty. In this work, we present an uncertainty-aware
segmentation model, BA U-Net, for use on MRI data that incorporates Bayesian
Neural Networks and Attention Mechanisms to provide accurate and interpretable
segmentations. We evaluated our model on the publicly available BraTS 2020
dataset using F1 Score and Intersection Over Union (IoU) as evaluation metrics.
