The development of an human immunodeficiency virus (HIV) test that detects recent infection has enabled the U.S. Centers for Disease Control and Prevention (CDC) to estimate annual HIV incidence (number of new infections per year, not per person at risk) in the United States from data on new HIV and acquired immunodeficiency syndrome (AIDS) diagnoses reported to HIV/AIDS surveillance. We developed statistical procedures to estimate the probability that an infected person will be detected as recently infected, accounting for individuals choosing whether and how frequently to seek HIV testing, variation of testing frequency, the reporting of test results only for infected persons, and infected persons who never had an HIV-negative test. The incidence estimate is the number of persons detected as recently infected divided by the estimated probability of detection. We used simulation to show that, under the assumptions we make, our procedures have acceptable bias and correct confidence interval coverage. Because data on the biomarker for recent infection or on testing history were missing for many persons, we used multiple imputation to apply our models to surveillance data. CDC has used these procedures to estimate HIV incidence in the United States.