Background: Melanoma incidence rates have been increasing in fair-skinned populations, with Australia having the world’s highest melanoma incidence rates. By providing individuals with their overall risk instead of relying on individual risk factors, melanoma risk prediction models may lead to improved risk perception and sun protection behaviours. In addition to their clinical uses, these models may assist in planning intervention trials and population prevention strategies that target particular risk groups. We aimed to develop and validate a melanoma risk model predicting lifetime absolute risk of primary melanoma using self-assessed risk factors. Methods: We used unconditional logistic regression with backward selection to develop the melanoma risk model using the Australian Melanoma Family Study, a population-based case-control-family study with 629 population-based cases with first primary melanoma diagnosed before age 40 years and 535 controls from 2001 to 2005. Relative risk estimates from the model were combined with Australian melanoma incidence and mortality data using the Gail method to obtain lifetime absolute risk estimates. Subsequently we validated the model externally using the Western Australia Melanoma Study, a population-based study with 511 case-control pairs from 1980 to 1981. Multiple imputation was used to handle missing data. Results: Our model, which includes age, sex, state of residence, hair colour, naevus density, first degree family history of melanoma, previous non-melanoma skin cancer and lifetime sunbed use, demonstrated good discriminative performance on both internal [area under the receiver operating curve (AUC) = 0.71 (95% CI 0.68 - 0.73)] and external validation [AUC = 0.63 (0.60 – 0.65)]. Conclusions: The model, which is based on self-assessed risk factors, discriminates well between those with and without melanoma and may be useful in the design of melanoma prevention interventions.