  In this paper, we develop a metric designed to assess and rank uncertainty
measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019
sub-challenge on uncertainty quantification. The metric is designed to: (1)
reward uncertainty measures where high confidence is assigned to correct
assertions, and where incorrect assertions are assigned low confidence and (2)
penalize measures that have higher percentages of under-confident correct
assertions. Here, the workings of the components of the metric are explored
based on a number of popular uncertainty measures evaluated on the BraTS 2019
dataset.
