  In recent years, deep learning has been applied to a wide range of medical
imaging and image processing tasks. In this work, we focus on the estimation of
epistemic uncertainty for 3D medical image-to-image translation. We propose a
novel model uncertainty quantification method, Multi-Axis Fusion (MAF), which
relies on the integration of complementary information derived from multiple
views on volumetric image data. The proposed approach is applied to the task of
synthesizing contrast enhanced T1-weighted images based on native T1, T2 and
T2-FLAIR scans. The quantitative findings indicate a strong correlation
($\rho_{\text healthy} = 0.89$) between the mean absolute image synthetization
error and the mean uncertainty score for our MAF method. Hence, we consider MAF
as a promising approach to solve the highly relevant task of detecting
synthetization failures at inference time.
