<p>Fine-grained image classification remains challenging due to high intra-class variation and strong inter-class similarity, which make it difficult to obtain well-structured output distributions. While most existing approaches address this problem through spatial modeling or feature-level representation learning, output-level processing remains relatively underexplored despite its scalability across backbone architectures. To address this limitation, this paper proposes a self-calibrated mutual learning framework that improves performance in fine-grained contexts by reshaping output distribution geometry through the interaction between cross-model consistency and self-calibration. Cross-model consistency is achieved via deep mutual learning, while self-calibration is induced using online label smoothing. Unlike a simple combination of two loss terms, the proposed framework jointly enforces sample-wise consistency and class-wise target regularization within a unified probability space. Experiments on three benchmark datasets across multiple backbone architectures demonstrate that the proposed method consistently improves classification accuracy over existing output-level mutual learning methods and produces a complementary effect beyond their individual contributions.</p>