In regions with high intermittent renewable energy share, thermal plants are forced to operate with greater flexibility beyond their original design intent. Decreasing energy prices and capacity factors will further force these plants to more transient operation with steeper load gradients. Older steam turbine (ST) protections systems on site are often not designed for such flexible operation and do not properly supervise the resulting impact on lifetime consumption. Therefore, precise lifetime management concepts are required to increase plant reliability and flexibility, and to mitigate risks for new implemented operation modes. Several lifetime assessment methods were developed to quantify the damage evolution and the residual lifetime for ST components. Usually, these methods require both input about representative or operated loading profiles and characteristic material curves. These characteristic curves are determined by a number of standardized material tests. Due to the material scatter and other different sources of uncertainties, each test is a realization of a stochastic process. Hence, the corresponding characteristic material curves inherit these uncertainties and do not represent an absolute limit. The analyses of different loading profiles even for the same plant and same start-up class reveal that the consideration of statistically evaluated specific start-up distributions and further transient events are of major importance. Probabilistic methods are able to quantify all of these uncertainties and compute the probability of failure for a given lifetime or vice versa. Within this paper, at first an extensive and systematic operational profile analysis is carried out and discussed, which acts as an input for a probabilistic lifetime assessment approach. For that, a developed probabilistic workflow is presented to quantify the uncertainties and for lifetime prediction using the generalized damage accumulation rule with focus on creep-fatigue loading. To quantify the characteristic material curves, existing experimental data of a 2%-Cr forged steel (23CrMoNiWV8-8) is used. A probabilistic representation of the Wilshire–Scharning equation characterizes the creep rupture behavior. The maximum-likelihood method is used for parameter estimation and to take still running long term creep experiments into account. The end of life in low cycle fatigue experiments is characterized by a macroscopic crack initiation, and the Manson–Coffin–Basquin equation is utilized to represent the characteristic material curve. A temperature-modified version of the Manson–Coffin–Basquin equation is used to represent the experimental data. The parameter estimation is done by using the linear regression analysis followed by a comprehensive regression diagnostic. Taking both the material and the load scatter into account, a reliability analysis is carried out to compute the probability of crack initiation. Finally, different load cases are considered and evaluated against each other.