Code assistants powered by large language models are now embedded in integrated development environments, yet developers lack reliable signals for when to trust generated code. Model confidence could serve as a signal, but only if it accurately reflects the likelihood of acceptance. Post-hoc calibration aims to achieve this alignment, though its efficacy in production settings remains understudied. We investigate in-IDE confidence calibration from two perspectives: (1) scalable methods for calibrating confidence signals and (2) interface design for communicating reliability to developers. We introduce a flexible calibration framework for open-source models and evaluate calibration against developer acceptance behavior using over 24 million real-world IDE interactions across multiple languages. We find that a general Platt-scaling calibrator does not, consistently improve the usefulness of confidence as a reliability signal, while personalized calibration can help when sufficient user interaction data is available. Complementing this, a multi-phase design study with expert designers and 153 professional developers indicates a preference for non-numerical, color-coded reliability indicators embedded in the in-editor generation workflow.