A new human-AI collaboration method called judgment-grounded expansion enables accountable peer review generation. The approach involves a reviewer providing an evaluative claim, which the system expands into review comment candidates through a structured generate-check-refine process. The study addresses scalable evaluation and candidate set curation, showing conformal prediction effectively balances candidate size and coverage.