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While multiple studies have explored the relation between inter-ratervariability and deep learning model uncertainty in medical segmentation tasks,little is known about the impact of individual rater style. This studyquantifies rater style in the form of bias and consistency and explores theirimpacts when used to train deep learning models. Two multi-rater publicdatasets were used, consisting of brain multiple sclerosis lesion and spinalcord grey matter segmentation. On both datasets, results show a correlation($R^2 = 0.60$ and $0.93$) between rater bias and deep learning uncertainty. Theimpact of label fusion between raters' annotations on this relationship is alsoexplored, and we show that multi-center consensuses are more effective thansingle-center consensuses to reduce uncertainty, since rater style is mostlycenter-specific.

Impact of individual rater style on deep learning uncertainty in medical imaging segmentation