  Classifiers for medical image analysis are often trained with a single
consensus label, based on combining labels given by experts or crowds. However,
disagreement between annotators may be informative, and thus removing it may
not be the best strategy. As a proof of concept, we predict whether a skin
lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd
annotations of visual characteristics of that lesion. We compare using the mean
annotations, illustrating consensus, to standard deviations and other
distribution moments, illustrating disagreement. We show that the mean
annotations perform best, but that the disagreement measures are still
informative. We also make the crowd annotations used in this paper available at
\url{https://figshare.com/s/5cbbce14647b66286544}.
