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Ensemble Learning is a machine learning method that enhances overall predictive performance by combining multiple base learners. However, most current ensemble learning approaches employ average fusion methods, which overlook the consistency and diversity of individual model predictions and are unable to adaptively handle testing data. This paper introduces an Evidence Reasoning (ER) rule ensemble learning method that unifies model adaptation, uncertainty estimation, and confidence calibration within a single framework, thereby providing a more reliable model to aid physicians in decision-making. We evaluated our approach in predicting locoregional recurrence in Head and Neck Cancer (HNC). Compared to the previously proposed ERE, the ER-rule ensemble model achieved a 4.1% improvement in ACC.Clinical Relevance-This ER-rule ensemble model demonstrates a more reliable approach to predicting locoregional recurrence in head and neck cancer, enabling timely clinical intervention and potentially improving patient outcomes.