  This paper focuses on the uncertainty estimation for white matter lesions
(WML) segmentation in magnetic resonance imaging (MRI). On one side,
voxel-scale segmentation errors cause the erroneous delineation of the lesions;
on the other side, lesion-scale detection errors lead to wrong lesion counts.
Both of these factors are clinically relevant for the assessment of multiple
sclerosis patients. This work aims to compare the ability of different voxel-
and lesion-scale uncertainty measures to capture errors related to segmentation
and lesion detection, respectively. Our main contributions are (i) proposing
new measures of lesion-scale uncertainty that do not utilise voxel-scale
uncertainties; (ii) extending an error retention curves analysis framework for
evaluation of lesion-scale uncertainty measures. Our results obtained on the
multi-center testing set of 58 patients demonstrate that the proposed
lesion-scale measure achieves the best performance among the analysed measures.
All code implementations are provided at
https://github.com/NataliiaMolch/MS_WML_uncs
