  Measuring uncertainties in the output of a deep learning method is useful in
several ways, such as in assisting with interpretation of the outputs, helping
build confidence with end users, and for improving the training and performance
of the networks. Several different methods have been proposed to estimate
uncertainties, including those from epistemic (relating to the model used) and
aleatoric (relating to the data) sources using test-time dropout and
augmentation, respectively. Not only are these uncertainty sources different,
but they are governed by parameter settings (e.g., dropout rate or type and
level of augmentation) that establish even more distinct uncertainty
categories. This work investigates how different the uncertainties are from
these categories, for magnitude and spatial pattern, to empirically address the
question of whether they provide usefully distinct information that should be
captured whenever uncertainties are used. We take the well characterised BraTS
challenge dataset to demonstrate that there are substantial differences in both
magnitude and spatial pattern of uncertainties from the different categories,
and discuss the implications of these in various use cases.
