Stance classifiers make errors that can be costly to identify under limited human-review budgets. Prioritizing potentially incorrect predictions remains challenging, particularly across heterogeneous base models. We introduce B-CARE (Blind Classwise Assessment of Relative Evidence for Stance Error Ranking), a prediction-blind framework for identifying high-risk stance predictions. Given only a target and text, a fixed LLM verifier assigns ordinal evidence scores to all candidate stance labels without observing the base model's prediction or confidence. B-CARE constructs a comparative risk score by contrasting the strongest alternative-class evidence with the evidence supporting the predicted class. The resulting classwise evidence profile can be reused across multiple base models without training a separate error detector or calibrating risk scores. We evaluate B-CARE on four stance benchmarks and three heterogeneous base LLMs, covering 12 dataset-model settings and 5,206 unique test instances. Evaluation includes error-ranking discrimination (AUROC and AUPRC), selective prediction (E-AURC), and review effectiveness (ErrorCapture at fixed review budgets). B-CARE achieves a mean AUROC of 0.735 and a mean AUPRC of 0.524, capturing 41.06% of errors within the highest-risk 20% of predictions. Although B-CARE does not uniformly outperform strong confidence-based baselines, it provides a reusable, complementary evidence signal for prioritizing potentially incorrect stance predictions across heterogeneous models.

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