Zero-shot vision-language models (VLMs) such as CLIP demonstrate strong open-vocabulary recognition capabilities, yet their prediction confidences are often poorly calibrated. Existing calibration methods often estimate prediction stability by applying multiple augmentations in input space, introducing substantial computational overhead and limiting practical deployment. To this end, this paper proposes an embedding-space posterior calibration approach for zero-shot VLMs, which shifts the sampling process from the input space to the embedding space. Our approach defines and computes a confidence stability score that measures how stable the sample remains under posterior embedding perturbations. This score is further converted into a sample-wise temperature, so that unstable samples receive stronger confidence smoothing while stable samples remain close to their original predictions. Compared with the original CLIP model, our method reduces the mean Expected Calibration Error from 5.83 to 2.05, achieving a 64.8% relative reduction. Meanwhile, our method achieves this improvement with only a minor increase in GFLOPs, from 8.83 to 8.84.