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.

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