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Medical image segmentation annotations suffer from inter- and intra-observervariations even among experts due to intrinsic differences in human annotatorsand ambiguous boundaries. Leveraging a collection of annotators' opinions foran image is an interesting way of estimating a gold standard. Although trainingdeep models in a supervised setting with a single annotation per image has beenextensively studied, generalizing their training to work with datasetscontaining multiple annotations per image remains a fairly unexplored problem.In this paper, we propose an approach to handle annotators' disagreements whentraining a deep model. To this end, we propose an ensemble of Bayesian fullyconvolutional networks (FCNs) for the segmentation task by considering twomajor factors in the aggregation of multiple ground truth annotations: (1)handling contradictory annotations in the training data originating frominter-annotator disagreements and (2) improving confidence calibration throughthe fusion of base models' predictions. We demonstrate the superior performanceof our approach on the ISIC Archive and explore the generalization performanceof our proposed method by cross-dataset evaluation on the PH2 and DermoFitdatasets.

D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation