As large language models (LLMs) find increasing use in critical applications, evaluating their ability to assess their own outputs has become crucial. We present a theoretical and empirical framework that examines whether LLMs can differentiate between correct and incorrect solutions while maintaining properly calibrated confidence. Building on classical Vapnik-Chervonenkis (VC) theory, we adapt it to probabilistic predictors through two complexity metrics: Probabilistic VC (PVC), which measures a model's ability to confidently classify across problem types, and Calibration-aware PVC (C-PVC), which additionally demands alignment between confidence scores and actual success rates. Unlike Expected Calibration Error (ECE) and Actual Error (AE), these measures give an integrated assessment of self-evaluation expressiveness and calibration, with sample complexity bounds and generalization guarantees comparable to classical VC theory. We test eleven 7-8B models on 360 original mathematical reasoning problems, TruthfulQA, and CommonsenseQA. Each model chooses between two of its own generated solutions and reports its confidence, with ground truth determined by an ensemble of larger models. The results show a systematic inverse relationship: models with greater self-evaluation expressiveness have weaker calibration. s1.1-7B and Qwen2.5-7B-Instruct achieve the highest PVC-VUS scores, while JiuZhang3.0-7B is best calibrated, with the lowest ECE and the smallest PVC-VUS gap. The trade-off persists across architectures, training paradigms, and domains, pointing to a fundamental challenge in developing self-reflective LLMs, and our framework offers practical tools for diagnosing it.

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