  Variability in medical image segmentation, arising from annotator
preferences, expertise, and their choice of tools, has been well documented.
While the majority of multi-annotator segmentation approaches focus on modeling
annotator-specific preferences, they require annotator-segmentation
correspondence. In this work, we introduce the problem of segmentation style
discovery, and propose StyleSeg, a segmentation method that learns plausible,
diverse, and semantically consistent segmentation styles from a corpus of
image-mask pairs without any knowledge of annotator correspondence. StyleSeg
consistently outperforms competing methods on four publicly available skin
lesion segmentation (SLS) datasets. We also curate ISIC-MultiAnnot, the largest
multi-annotator SLS dataset with annotator correspondence, and our results show
a strong alignment, using our newly proposed measure AS2, between the predicted
styles and annotator preferences. The code and the dataset are available at
https://github.com/sfu-mial/StyleSeg.
