Class-conditional conformal prediction calibrates a separate score threshold for every class and therefore requires labelled calibration examples of every class. In long-tailed deployments these are exactly what is missing: rare classes are left with few or no held-out examples, and existing methods are either undefined for them or fall back to the shared marginal threshold. We ask whether the per-class correction can instead be predicted from descriptors of the class that require no labels at all. We propose Predicted Class Correction (PCC), which regresses the offset between the per-class and the marginal conformal quantile on geometric descriptors of the classifier head, shrinks the prediction by a scalar selected on the labelled classes, and refits a single marginal offset. The descriptors depend only on the model's weights, so a class with zero calibration examples still receives its own threshold, and since only one scalar is calibrated, split conformal's marginal guarantee is retained. On ImageNet, PCC improves worst-class coverage on such classes by 0.058 [0.006, 0.110] at matched average set size, 42% of an oracle that sees the test labels. Of eight baselines on the same splits, six change the statistic by exactly zero and the best reaches 0.016 with an interval containing zero, so PCC is the only one distinguishable from no improvement; the gain persists with only 100 labelled classes out of 1000. A second finding concerns evaluation: worst-class coverage becomes unmeasurable before it becomes unachievable. Below roughly 35 test examples per class no improvement can be shown even for an oracle, so a null result there reflects the evaluation set, not the method. Restricting evaluation above this depth recovers a significant improvement on Pl@ntNet-300K, where the effect had appeared absent.

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