In human-AI decision-making, understanding the factors that maximize overall accuracy remains a critical challenge. This study highlights the role of metacognitive sensitivity—the agent's ability to assign confidence scores that reliably distinguish between correct and incorrect predictions. We propose a theoretical framework to evaluate the impact of accuracy and metacognitive sensitivity in hybrid decision-making contexts. Our analytical results establish conditions under which an agent with lower accuracy but higher metacognitive sensitivity can enhance overall decision accuracy when paired with another agent. Empirical analyses on a real-world image classification dataset confirm that stronger metacognitive sensitivity—whether in AI or human agents—can improve joint decision outcomes. These findings advocate for a more comprehensive approach to evaluating AI and human collaborators, emphasizing the joint optimization of accuracy and metacognitive sensitivity for enhanced decision-making.