In the field of histopathology, semantic segmentation is essential for the diagnosis of diseases, the evaluation of treatment effectiveness, and the prediction of outcomes. However, the task of assigning pixel-level labels to histopathology images manually is a process that requires significant time and effort. In this work, we introduce an advanced semi-supervised framework for semantic segmentation in histopathology imaging, called AEL-SPST, which effectively merges Adaptive Equalization Learning (AEL) and Selective Progressive Self-Training (SPST). Overcoming the intrinsic difficulties posed by scarce labeled data and class imbalance in histopathology images. The AEL adopts a dual-network structure, where the instructor network is tasked with producing pseudo-labels, and the learner network performs online learning. Its core component, Adaptive Equalization Sampling (AES), is central to monitoring and dynamically adapting to category-specific performance during training, with the aim of improving segmentation accuracy for underrepresented classes. For the pseudo-label generation process, SPST employs a reliability assessment mechanism to filter and rank the pseudo-labels based on their consistency and accuracy. The framework selectively retrains the model, first focusing on the reliable pseudo-labels and subsequently incorporating the less reliable ones. This approach not only enhances the robustness and precision of pseudo-labels but also significantly reduces reliance on extensive labeled datasets. In more intuitive terms, AEL structures the instructional methodology, while SPST cultivates a curriculum-based learning process that gradually assimilates complex data. Rigorous experimental assessments conducted across two difficult datasets, BCSS and LUAD-HistoSeg, demonstrates our method’s outstanding performance, achieving average improvements of 3.5% and 6.35% respectively.