Over the past decade, U-Net has been the dominant architecture in medical
image segmentation, leading to the development of thousands of U-shaped
variants. Despite its widespread adoption, there is still no comprehensive
benchmark to systematically evaluate their performance and utility, largely
because of insufficient statistical validation and limited consideration of
efficiency and generalization across diverse datasets. To bridge this gap, we
present U-Bench, the first large-scale, statistically rigorous benchmark that
evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our
contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates
models along three key dimensions: statistical robustness, zero-shot
generalization, and computational efficiency. We introduce a novel metric,
U-Score, which jointly captures the performance-efficiency trade-off, offering
a deployment-oriented perspective on model progress. (2) Systematic Analysis
and Model Selection Guidance: We summarize key findings from the large-scale
evaluation and systematically analyze the impact of dataset characteristics and
architectural paradigms on model performance. Based on these insights, we
propose a model advisor agent to guide researchers in selecting the most
suitable models for specific datasets and tasks. (3) Public Availability: We
provide all code, models, protocols, and weights, enabling the community to
reproduce our results and extend the benchmark with future methods. In summary,
U-Bench not only exposes gaps in previous evaluations but also establishes a
foundation for fair, reproducible, and practically relevant benchmarking in the
next decade of U-Net-based segmentation models. The project can be accessed at:
https://fenghetan9.github.io/ubench. Code is available at:
https://github.com/FengheTan9/U-Bench.