In digital advertising, online platforms allocate ad impressions through real-time auctions, where advertisers typically rely on autobidding agents to optimize bids on their behalf. Unlike traditional auctions for physical goods, the value of an ad impression is uncertain and depends on the unknown click-through rate (CTR). While platforms can estimate CTRs more accurately using proprietary machine learning algorithms, these estimates/algorithms remain opaque to advertisers. This information asymmetry naturally raises the following questions: how can platforms disclose information in a way that is both credible and revenue-optimal? We address these questions through calibrated signaling, where each prior-free bidder receives a private signal that truthfully reflects the conditional expected CTR of the ad impression. Such signals are trustworthy and allow bidders to form unbiased value estimates, even without access to the platform's internal algorithms. We study the design of platform-optimal calibrated signaling in the context of second-price auction. Our first main result fully characterizes the structure of the optimal calibrated signaling, which can also be computed efficiently. We show that this signaling can extract the full surplus -- or even exceed it -- depending on a specific market condition. Our second main result is an FPTAS for computing an approximately optimal calibrated signaling that satisfies an IR condition. Our main technical contributions are: a reformulation of the platform's problem as a two-stage optimization problem that involves optimal transport subject to calibration feasibility constraints on the bidders' marginal bid distributions; and a novel correlation plan that constructs the optimal distribution over second-highest bids.