Title: Semi-Supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data. Abstract: We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We firstly figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution. To address this issue, we theoretically analyze and experimentally prove that appropriately boosting uncertainty on unlabeled data can help minimize the distribution gap, which benefits the generalization of the model. We propose two strategies and design an algorithm of uncertainty booster specially for semi-supervised semantic segmentation. Extensive experiments are carried out based on these theories, and the results confirmed the efficacy of the algorithm and strategies. Our plug-and play uncertainty booster is tiny, efficient and robust to hyper parameters, but can significantly promote the performance. In our experiments, our method achieves state-of-the-art performance compared to the current semi-supervised semantic segmentation methods on the popular benchmark: Cityscapes and PASCAL VOC 2012 with different train settings.