Deep learning-based MR image reconstruction from undersampled data bears the risk of inducing reconstruction errors like in-painting of non-anatomical structures, or missing pathologies. These errors may be obscured by the deep learning process and thus remain undiscovered. Furthermore, most methods are task-specialized and not well calibrated to domain shifts. Thus, integrated uncertainty prediction would be desirable. We propose a deep ensembling strategy that allows us to assess potential algorithm failures and better adapt to changing scenarios. The proposed approach can be paired with any DL reconstruction, enabling investigations of their predictive uncertainties on a voxel-level.
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