Summary: A conditional score approach is proposed to the analysis of errors-in-variables current status data under the proportional odds model. Distinct from the conditional scores in other applications, the proposed conditional score involves a high-dimensional nuisance parameter, causing challenges in both asymptotic theory and computation. We propose a composite algorithm combining the Newton-Raphson and self-consistency algorithms for computation and develop an efficient conditional score, analogous to the efficient score from a typical semiparametric likelihood, for building an asymptotic linear expression and hence the asymptotic distribution of the conditional-score estimator for the regression parameter. Our proposal is shown to perform well in simulation studies and is applied to zebrafish basal cell carcinoma data involving measurement errors in gene expression levels.