  Recent studies have shown that ensemble approaches could not only improve
accuracy and but also estimate model uncertainty in deep learning. However, it
requires a large number of parameters according to the increase of ensemble
models for better prediction and uncertainty estimation. To address this issue,
a generic and efficient segmentation framework to construct ensemble
segmentation models is devised in this paper. In the proposed method, ensemble
models can be efficiently generated by using the stochastic layer selection
method. The ensemble models are trained to estimate uncertainty through
Bayesian approximation. Moreover, to overcome its limitation from uncertain
instances, we devise a new pixel-wise uncertainty loss, which improves the
predictive performance. To evaluate our method, comprehensive and comparative
experiments have been conducted on two datasets. Experimental results show that
the proposed method could provide useful uncertainty information by Bayesian
approximation with the efficient ensemble model generation and improve the
predictive performance.
