  The annotation of domain experts is important for some medical applications
where the objective ground truth is ambiguous to define, e.g., the
rehabilitation for some chronic diseases, and the prescreening of some
musculoskeletal abnormalities without further medical examinations. However,
improper uses of the annotations may hinder developing reliable models. On one
hand, forcing the use of a single ground truth generated from multiple
annotations is less informative for the modeling. On the other hand, feeding
the model with all the annotations without proper regularization is noisy given
existing disagreements. For such issues, we propose a novel Learning to
Agreement (Learn2Agree) framework to tackle the challenge of learning from
multiple annotators without objective ground truth. The framework has two
streams, with one stream fitting with the multiple annotators and the other
stream learning agreement information between annotators. In particular, the
agreement learning stream produces regularization information to the classifier
stream, tuning its decision to be better in line with the agreement between
annotators. The proposed method can be easily added to existing backbones, with
experiments on two medical datasets showed better agreement levels with
annotators.
