  The Jaccard index, also known as Intersection-over-Union (IoU score), is one
of the most critical evaluation metrics in medical image segmentation. However,
directly optimizing the mean IoU (mIoU) score over multiple objective classes
is an open problem. Although some algorithms have been proposed to optimize its
surrogates, there is no guarantee provided for their generalization ability. In
this paper, we present a novel data-distribution-aware margin calibration
method for a better generalization of the mIoU over the whole
data-distribution, underpinned by a rigid lower bound. This scheme ensures a
better segmentation performance in terms of IoU scores in practice. We evaluate
the effectiveness of the proposed margin calibration method on two medical
image segmentation datasets, showing substantial improvements of IoU scores
over other learning schemes using deep segmentation models.
