Existing semi-supervised learning methods typically mitigate the impact of unreliable predictions by suppressing low-confidence regions. However, these methods fail to explore which regions hold higher learning value and how to design adaptive learning strategies for these regions, thereby limiting the model's performance in critical areas. To address this issue, we propose a novel adaptive learning of high-value regions (ALHVR) framework. By exploiting the diversity of predictions from mutli-branch networks, the prediction regions are classified into three types: reliable stable region, reliable unstable region, and unreliable stable region. For high-value regions (reliable unstable region and unreliable stable region), different training strategies are designed. Specifically, for reliable unstable region, we propose a confidence-guided cross-prototype consistency learning (CG-CPCL) module, which enforces prototype consistency constraints in the feature space. By leveraging confidence information, the high-confidence predictions from one network selectively supervise the low-confidence predictions of the other, thus helping the model learn inter-class discrimination more stably. Additionally, for unreliable stable region, we design a dynamic teacher competition teaching (DTCT) module, which dynamically selects the most reliable pixels as teachers by evaluating the unperturbed predictions from both networks in real-time. These selected pixels are then used to supervise perturbed predictions, thereby enhancing the model's learning capability in unreliable region. Experimental results demonstrate that the proposed method outperforms state-of-the-art semi-supervised learning approaches on three datasets including ACDC, AbdomenCT-1K, and Brats. The code will be available at XXX.