  Despite the superior performance of Deep Learning (DL) on numerous
segmentation tasks, the DL-based approaches are notoriously overconfident about
their prediction with highly polarized label probability. This is often not
desirable for many applications with the inherent label ambiguity even in human
annotations. This challenge has been addressed by leveraging multiple
annotations per image and the segmentation uncertainty. However, multiple
per-image annotations are often not available in a real-world application and
the uncertainty does not provide full control on segmentation results to users.
In this paper, we propose novel methods to improve the segmentation probability
estimation without sacrificing performance in a real-world scenario that we
have only one ambiguous annotation per image. We marginalize the estimated
segmentation probability maps of networks that are encouraged to
under-/over-segment with the varying Tversky loss without penalizing balanced
segmentation. Moreover, we propose a unified hypernetwork ensemble method to
alleviate the computational burden of training multiple networks. Our
approaches successfully estimated the segmentation probability maps that
reflected the underlying structures and provided the intuitive control on
segmentation for the challenging 3D medical image segmentation. Although the
main focus of our proposed methods is not to improve the binary segmentation
performance, our approaches marginally outperformed the state-of-the-arts. The
codes are available at \url{https://github.com/sh4174/HypernetEnsemble}.
