# Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation

arXiv: https://arxiv.org/abs/2005.10754 · HTML: https://mlsanity.com/paper/2005.10754/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 5 of 5)

- [Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation](https://mlsanity.com/paper/2303.06920/) (2023)
- [Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation](https://mlsanity.com/paper/2412.16990/) (2024)
- [Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling](https://mlsanity.com/paper/2401.09245/) (2024)
- [Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation](https://mlsanity.com/paper/2503.22271/) (2025)
- [Uncertainty evaluation of segmentation models for Earth observation](https://mlsanity.com/paper/2510.19586/) (2025)

## Abstract (arXiv)

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.