A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate their confidence, enabling systems to determine when to trust model outputs and when to seek human intervention. In this paper, we present a Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs, a framework that combines structured reasoning with distance-aware calibration techniques. Our approach introduces three key innovations: (1) a Maximum Confidence Selection (MCS) method that comprehensively evaluates confidence across all possible labels, (2) a reflection-based prompting mechanism that enhances reasoning reliability, and (3) a distance-aware calibration technique that accounts for ordinal relationships between labels. We evaluate our framework across diverse datasets, including HelpSteer2, Llama T-REx, and an internal conversational dataset, demonstrating its effectiveness across both conversational and fact-based classification tasks. This work contributes to the broader goal of developing reliable and well-calibrated confidence estimation methods for LLMs, enabling informed decisions about when to trust model outputs and when to defer to human judgement.