Semi-supervised medical image segmentation (SSMIS) has shown promise in model training for limited medical labeling data. However, the dominant teacher-student modeling approach is subject to confirmation bias due to erroneous pseudo-labeling effects. In this research we present the Adaptive Diversity-Alternated Teaching (ADAT) framework, which is based on the average teacher approach and involves one student and two teachers. The framework integrates labeled and unlabeled data into a dual-teacher network for adaptive alternating instruction, leveraging inter-teacher prediction discrepancies to dynamically guide multi-perspective student learning. Specifically, ADAT dynamically coordinates dual-teacher instruction through predictive entropy weights in the Diverse Alternate Information (DAI) Exchanging Module. Hybrid images are generated by foreground-background swapping between labeled/unlabeled data batches, ensuring teaching diversity via distinct teacher inputs. Meanwhile, the Adaptive Discrepancy Module (ADM) categorizes teacher predictions into consensus and discrepancy regions for targeted student learning. By comparing the ratio of these parts, we adaptively adjust the learning degree of student at congruent and discrepant parts. The Dice Score is 2.51% greater than the current State-of-the-Art approach on the ACDC dataset with 5% labeled data, according to experiments conducted on various public medical image datasets. Experiments across multiple benchmarks demonstrate that our method achieves state-of-the-art or competitive performance, setting new performance benchmarks in SSMIS.